Jacques Demerjian

Critique on Natural Noise in Recommender Systems

2021 · ACM Transactions on Knowledge Discovery from Data

Recommender systems have been upgraded, tested, and applied in many, often incomparable ways. In attempts to diligently understand user behavior in certain environments, those systems have been frequently utilized in domains like e-commerce, e-learning, and …

The Cost of Interpretability: Auditable User Representations via Archetypes

2026 · Zenodo (CERN European Organization for Nuclear Research)

Collaborative filtering embeddings underpin personalization across web platforms but are opaque: a user's dense vector cannot explain their behavioral profile to a regulator or the user themselves, and retraining produces arbitrarily rotated coordinates that defeat …

Similarity measures for collaborative filtering recommender systems

2018

Collaborative filtering recommender systems evaluate users' ratings in order to give them better recommendations. One of the popular ways to make rating predictions is by using neighborhood-based models which rely on calculating the similarities between …